Functional semantics annotation of assembly model using the fusion of bag of relationships model and spectral technology

Abstract

Increasing attention has been paid to the effective management and reuse of CAD model resources. Aiming at the problems of low efficiency of model reuse and poor accuracy in the function labeling process of assembly models, this paper presents an effective probability-based model labeling strategy for complex assemblies. It is a proper method to realize automatic labeling of assembly functional semantics through active learning. Different from part model retrieval, an assembly model is described through graph theory and a bag-of-relationships model. The assembly relationships of assembly model and the information of key functional parts are considered synthetically. Then a two-tiered model retrieval mechanism is constructed to reduce the computation time cost and improve retrieval efficiency. Further, the concept of functional ontology is introduced to establish the normalized expression of the key functional semantics of the assembly model. The functional semantic annotation of the key parts of the CAD assembly model is carried out through a probability-based labeling framework to map the model shape structure to functional semantics, thus mitigating the “semantic gap” problem. Experimental results demonstrated that this method could improve the accuracy of functional semantic annotation of the assembly model, reduce the difficulty of labeling, and improve the reusability of 3D CAD models as a design resource.

Keywords

• automatic annotation
• key functional semantics
• assembly model labeling
• cad model retrieval
• model reuse